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Machine Learning in Communications: A Road to Intelligent Transmission and Processing

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arxiv 2407.11595 v2 pith:BEKJEZRL submitted 2024-07-16 eess.SP

classification eess.SP
keywords communicationsintelligentlearningmachinewirelessartificialdataintelligence
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Prior to the era of artificial intelligence and big data, wireless communications primarily followed a conventional research route involving problem analysis, model building and calibration, algorithm design and tuning, and holistic and empirical verification. However, this methodology often encountered limitations when dealing with large-scale and complex problems and managing dynamic and massive data, resulting in inefficiencies and limited performance of traditional communication systems and methods. As such, wireless communications have embraced the revolutionary impact of artificial intelligence and machine learning, giving birth to more adaptive, efficient, and intelligent systems and algorithms. This technological shift opens a road to intelligent information transmission and processing. This overview article discusses the typical roles of machine learning in intelligent wireless communications, as well as its features, challenges, and practical considerations.

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Cited by 1 Pith paper

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  1. Towards a Foundation Model for Communication Systems

    cs.AI 2025-05 conditional novelty 6.0 of 10

    A single pre-trained transformer can forecast and interpolate multiple wireless channel features (rank, precoder, Doppler, delay) on simulated 5G NR data.

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